Saga Ishtardottir writes that CyberTiel is an abliterated, quantized MoE model designed for high-speed agentic coding and offensive security tasks. Based on the Ornith-1.5-35B-A3B architecture, this version features a grafted multi-token prediction (MTP) head to enable speculative decoding via llama.cpp and uses a custom cyber-weighted importance matrix to maintain performance in low-bit quantizations. The model is optimized for real-world programming problems, showing significant improvements on benchmarks like SWE-bench-Live compared to its censored counterparts.
- Includes an abliterated (uncensored) base that allows the model to perform offensive security work without refusal.
- Features a grafted MTP head at block 40 specifically designed for speculative decoding speedups in llama.cpp.
- Uses Unsloth Dynamic quantization with a custom imatrix calibrated on specialized cybersecurity and code datasets.
- Capable of vision tasks via the mmproj-BF16.gguf projector from Ornith's original model.
- Includes an abliterated (uncensored) base that allows the model to perform offensive security work without refusal.
- Features a grafted MTP head at block 40 specifically designed for speculative decoding speedups in llama.cpp.
- Uses Unsloth Dynamic quantization with a custom imatrix calibrated on specialized cybersecurity and code datasets.
- Capable of vision tasks via the mmproj-BF16.gguf projector from Ornith's original model.
This repository provides quantized GGUF versions of the Tiel coder model, which is a re-quantized and dynamically improved version of Ornith-1.5 35B designed specifically for agentic coding tasks. It excels at multi-turn conversations and solving codebase issues with performance comparable to Opus 4.6 medium in certain benchmarks.
- Optimized via an importance matrix (imatrix) using a calibration corpus heavily weighted toward code.
- Inherits the Ornith-1.5 vision tower, allowing for image-to-text tasks like analyzing screenshots of failing tests.
- Uses the Sharp chat template to produce more concise answers compared to its base model.
- Designed with a Mixture of Experts (MoE) architecture that enables fast inference and small KV cache footprint in RAM.
- Optimized via an importance matrix (imatrix) using a calibration corpus heavily weighted toward code.
- Inherits the Ornith-1.5 vision tower, allowing for image-to-text tasks like analyzing screenshots of failing tests.
- Uses the Sharp chat template to produce more concise answers compared to its base model.
- Designed with a Mixture of Experts (MoE) architecture that enables fast inference and small KV cache footprint in RAM.
Carolina Bento writes about Linear Discriminant Analysis (LDA), a supervised learning technique used for dimensionality reduction and pattern recognition. The article explains how LDA maximizes class separability by maximizing the ratio of between-class to within-class variance, making it particularly useful for simplifying high-dimensional datasets while preserving core characteristics. Through a real estate dataset example, the author demonstrates how to implement LDA using ScikitLearn to visualize property type clusters and identify key features that distinguish different types of properties.
- LDA is a supervised method, unlike Principal Component Analysis (PCA), which is unsupervised.
- The maximum number of Linear Discriminants that can be calculated is $K-1$, where $K$ is the number of classes.
- Key assumptions for LDA include linear separability of data and following a Gaussian distribution.
- It helps reduce overfitting by removing redundant features and minimizing noise in high-dimensional spaces.
- LDA is a supervised method, unlike Principal Component Analysis (PCA), which is unsupervised.
- The maximum number of Linear Discriminants that can be calculated is $K-1$, where $K$ is the number of classes.
- Key assumptions for LDA include linear separability of data and following a Gaussian distribution.
- It helps reduce overfitting by removing redundant features and minimizing noise in high-dimensional spaces.
Terence Tao writes about recent mathematical breakthroughs by Alpöge and Buckmaster regarding singularity formation in fluid dynamics. Building on previous work by Córdoba and Martínez-Zoroa, these researchers demonstrated finite-time blowup for the incompressible porous medium (IPM) equation, the two-dimensional Boussinesq equation, and the three-dimensional incompressible Euler equations using smooth forcing terms or initial data. Tao highlights that while full solutions to the Navier–Stokes global regularity problem remain unachieved by this method, it significantly increases their feasibility in the near future.
- The strategy involves iteratively constructing solutions by adding high-frequency corrections that cause singularities at a blowup time.
- Recent work from Ganeshram, Duruisseaux, and Anandkumar utilizes physics-informed neural networks (PINNs) to locate self-similar blowup profiles for Euler equations.
- A significant controversy exists regarding whether AI models may have "mined" human research results without proper attribution or credit.
- Tao describes the current status of some AI-generated proofs as being in a novel stage between a formally verified proof and a well-written, peer-reviewed paper.
- The strategy involves iteratively constructing solutions by adding high-frequency corrections that cause singularities at a blowup time.
- Recent work from Ganeshram, Duruisseaux, and Anandkumar utilizes physics-informed neural networks (PINNs) to locate self-similar blowup profiles for Euler equations.
- A significant controversy exists regarding whether AI models may have "mined" human research results without proper attribution or credit.
- Tao describes the current status of some AI-generated proofs as being in a novel stage between a formally verified proof and a well-written, peer-reviewed paper.
The Clay Mathematics Institute (CMI) has announced that the Navier-Stokes Millennium Prize Problem, which concerns the existence and smoothness of solutions in 3-dimensional Euclidean space, appears to have been settled. This announcement follows years of anticipation driven by breakthroughs in fluid dynamics and technological advancements in mathematical research. CMI noted its excitement regarding the potential for new human understanding resulting from this resolution.
- The Millennium Prize Problems were established in Paris in 2000 with a $1M prize attached to each unsolved problem.
- Solving these problems is intended to demonstrate that the frontier of mathematics remains open and accessible.
- Each successful solution must undergo an unhurried evaluation process by CMI to assign credit according to specific rules.
- The Millennium Prize Problems were established in Paris in 2000 with a $1M prize attached to each unsolved problem.
- Solving these problems is intended to demonstrate that the frontier of mathematics remains open and accessible.
- Each successful solution must undergo an unhurried evaluation process by CMI to assign credit according to specific rules.
OpenHuman is an open-source agent harness designed as a personal AI super intelligence, featuring local-first memory through Markdown trees in SQLite and orchestration capabilities via checkpointed graphs. It functions as a brain that builds persistent context from various data sources like email and calendars, acting as both an orchestrator for multi-agent workflows and a deep researcher with built-in web search and media generation tools.
- Features "Memory Trees" stored locally in Markdown format to create a Karpathy-style Obsidian wiki.
- Provides end-to-end encrypted agent-to-agent messaging using the Signal protocol.
- Supports visual, trigger-driven workflows that can be proposed by an AI and reviewed on a canvas.
- Includes a "Privacy Mode" which ensures no inference data leaves the user's machine when toggled.
- Features "Memory Trees" stored locally in Markdown format to create a Karpathy-style Obsidian wiki.
- Provides end-to-end encrypted agent-to-agent messaging using the Signal protocol.
- Supports visual, trigger-driven workflows that can be proposed by an AI and reviewed on a canvas.
- Includes a "Privacy Mode" which ensures no inference data leaves the user's machine when toggled.
Benjamin Nweke writes that traditional fraud detection relies on the assumption of a human actor, where deviations from established behavioral patterns serve as primary signals. While explainability tools like SHAP can effectively detail why specific transaction features (like amount or timing) trigger a risk score, they are insufficient for addressing "machine-to-machine mayhem" caused by autonomous agents. Because these agents lack human biological constraints and consistent life patterns, feature attribution on transactions fails to capture the underlying intent or decision-making trajectory of an agent that may be operating outside its delegated scope.
- Agentic AI fraud is characterized as a shift toward "machine-to-machine mayhem" where bots mimic legitimate shopping agents.
- Current explainability methods like SHAP focus on transaction features rather than the actor's underlying decision path or tool usage.
- 60% of industry professionals expect AI-mediated banking to diminish the effectiveness of traditional fraud defenses.
- Proposed regulatory responses include NIST's Agent Standards Initiative and Senator Mark Warner's proposed AI AGENT Act for establishing accountability through registries.
- Agentic AI fraud is characterized as a shift toward "machine-to-machine mayhem" where bots mimic legitimate shopping agents.
- Current explainability methods like SHAP focus on transaction features rather than the actor's underlying decision path or tool usage.
- 60% of industry professionals expect AI-mediated banking to diminish the effectiveness of traditional fraud defenses.
- Proposed regulatory responses include NIST's Agent Standards Initiative and Senator Mark Warner's proposed AI AGENT Act for establishing accountability through registries.
Benjamin Marie writes that while Qwen3.8 27B demonstrates superior accuracy across various tasks compared to the recently released Muse Glimmer—particularly in long-horizon agentic coding—Muse Glimmer offers significant advantages in memory efficiency due to its lower KV-cache consumption and shorter reasoning traces.
- Muse Glimmer's KV-cache uses approximately 4 times less memory than Qwen3.8.
- Qwen3.8 was evaluated specifically using xhigh thinking mode.
- The study examines the trade-offs between raw accuracy, token efficiency, and memory use.
- Muse Glimmer's KV-cache uses approximately 4 times less memory than Qwen3.8.
- Qwen3.8 was evaluated specifically using xhigh thinking mode.
- The study examines the trade-offs between raw accuracy, token efficiency, and memory use.
Benjamin Marie explores the trade-offs between accuracy and token efficiency when adjusting the reasoning effort settings in Qwen3.8 27B. By comparing configurations where thinking is disabled, set to low, medium, or xhigh, he examines whether increasing a model's "thinking" time provides significant performance gains relative to the added computational cost and memory usage.
- The study focuses on non-agentic tasks where prompts are evaluated as standalone problems.
- Higher reasoning effort can lead to significantly longer reasoning traces and increased generation time.
- Experiments were conducted using RTX Pro 6000 GPUs provided by Verda.
- The study focuses on non-agentic tasks where prompts are evaluated as standalone problems.
- Higher reasoning effort can lead to significantly longer reasoning traces and increased generation time.
- Experiments were conducted using RTX Pro 6000 GPUs provided by Verda.
Benjamin Marie writes that the effectiveness of an LLM in long-horizon agentic coding tasks depends heavily on the harness used to drive it rather than just the model itself. Through testing Qwen3.8 27B across three different interfaces—Mini-SWE Agent, Claude Code, and Pi—the author found that while specific configurations like "benchmaxxed" Pi can solve the highest number of tasks, other setups like Claude Code achieve better functional coverage (F2P). The study highlights how critical engineering choices, such as preserving reasoning traces or managing output token limits, are essential for successful agentic performance.
- The evaluation used DeepSWE 1.1, a benchmark comprising 113 long-horizon tasks from 91 open-source repositories.
- Performance varies significantly based on whether reasoning traces are preserved between turns and how context budgets are managed.
- Pi at medium effort was found to offer the best balance of efficiency and accuracy.
- Results were influenced by factors like session recovery, patch reliability, and output-token settings. author »
- The evaluation used DeepSWE 1.1, a benchmark comprising 113 long-horizon tasks from 91 open-source repositories.
- Performance varies significantly based on whether reasoning traces are preserved between turns and how context budgets are managed.
- Pi at medium effort was found to offer the best balance of efficiency and accuracy.
- Results were influenced by factors like session recovery, patch reliability, and output-token settings. author »
